[Coursera] Machine Learning by Stanford University
Instructor: Andrew Ng
π¬ 113 episodes
β° 20 hours
βοΈ 4.9 out of 5 - by 160,879 ratings
π completely FREE
π Link to course
#machinelearning #datascience #stanford #andrewng
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ππ‘ππ’π¬ ππππ₯π‘ππ‘πππ
Instructor: Andrew Ng
π¬ 113 episodes
β° 20 hours
βοΈ 4.9 out of 5 - by 160,879 ratings
π completely FREE
π Link to course
#machinelearning #datascience #stanford #andrewng
ββββββββββ
Join @realgroupforprogrammer for more
ππ‘ππ’π¬ ππππ₯π‘ππ‘πππ
Undergraduate Machine Learning (Nando de Freitas/University of British Columbia)
Author: prof Nando de Freitas
π¬ 33 lessons
β° 21 hours
An undergraduate machine learning course. Lectures are filmed and put on YouTube with the slides posted on the course website. The course assignments are posted as well (no solutions, though). De Freitas is now a full-time professor at the University of Oxford and receives praise for his teaching abilities in various forums. Graduate version available.
https://www.cs.ubc.ca/~nando/340-2012/index.php
#machinelearning #datascience #statistics
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ππ‘ππ’π¬ ππππ₯π‘ππ‘πππ
Author: prof Nando de Freitas
π¬ 33 lessons
β° 21 hours
An undergraduate machine learning course. Lectures are filmed and put on YouTube with the slides posted on the course website. The course assignments are posted as well (no solutions, though). De Freitas is now a full-time professor at the University of Oxford and receives praise for his teaching abilities in various forums. Graduate version available.
https://www.cs.ubc.ca/~nando/340-2012/index.php
#machinelearning #datascience #statistics
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ππ‘ππ’π¬ ππππ₯π‘ππ‘πππ
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undergraduate machine learning at UBC 2012
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Data 8: Foundations of Data Science
UC Berkeley, Fall 2022
The UC Berkeley Foundations of Data Science course combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design.
The course is offered in partnership with the UC Berkeley Division of Computing, Data Science, and Society.
β³ Duration: 15 weeks
β Slides, demos and videos for each lesson
All materials for the course, including the textbook and assignments, are available for free online under a Creative Commons license.
Note: Course has already started but you can start from beginning and access all learning materials.
π Course link: http://data8.org/fa22/
#data_science #datascience #Berkeley #data_analysis
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ππ‘ππ’π¬ ππππ₯π‘ππ‘πππ
UC Berkeley, Fall 2022
The UC Berkeley Foundations of Data Science course combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design.
The course is offered in partnership with the UC Berkeley Division of Computing, Data Science, and Society.
β³ Duration: 15 weeks
β Slides, demos and videos for each lesson
All materials for the course, including the textbook and assignments, are available for free online under a Creative Commons license.
Note: Course has already started but you can start from beginning and access all learning materials.
π Course link: http://data8.org/fa22/
#data_science #datascience #Berkeley #data_analysis
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ππ‘ππ’π¬ ππππ₯π‘ππ‘πππ
Data 8
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